

'''simple docstring'''






import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase__   		=      [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''enhancement''',
    '''new pipeline/model''',
    '''new scheduler''',
    '''wip''',
]


def 						_A	(       ):






 """simple docstring"""



 __lowercase				    =					Github(os.environ['''GITHUB_TOKEN'''] )
 __lowercase				    =					g.get_repo('''huggingface/diffusers''' )
 __lowercase				    =					repo.get_issues(state='''open''' )

 for issue in open_issues:
  __lowercase				    =					sorted(issue.get_comments()	,							key=lambda A__ : i.created_at	,							reverse=A__ )
  __lowercase				    =					comments[0] if len(A__ ) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
  ):
   # Closes the issue after 7 days of inactivity since the Stalebot notification.
   issue.edit(state='''closed''' )
  elif (
      "stale" in issue.get_labels()
      and last_comment is not None
      and last_comment.user.login != "github-actions[bot]"
  ):
   # Opens the issue if someone other than Stalebot commented.
   issue.edit(state='''open''' )
   issue.remove_from_labels('''stale''' )
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
  ):
   # Post a Stalebot notification after 23 days of inactivity.
   issue.create_comment(
       '''This issue has been automatically marked as stale because it has not had '''
       '''recent activity. If you think this still needs to be addressed '''
       '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
       '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
       '''are likely to be ignored.''' )
   issue.add_to_labels('''stale''' )


if __name__ == "__main__":
 main()




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      int   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						str 							= DPTConfig(embedding_type="""hybrid""")

			if "large" in checkpoint_url:
						a_						:						List[str] 							= 1_0_2_4
						a_						:						List[str] 							= 4_0_9_6
						a_						:						Tuple 							= 2_4
						a_						:						str 							= 1_6
						a_						:						List[str] 							= [5, 1_1, 1_7, 2_3]
						a_						:						str 							= [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
						a_						:						Any 							= (1, 3_8_4, 3_8_4)

			if "nyu" or "midas" in checkpoint_url:
						a_						:						int 							= 7_6_8
						a_						:						List[str] 							= [1, 1, 1, 0.5]
						a_						:						Tuple 							= [2_5_6, 5_1_2, 7_6_8, 7_6_8]
						a_						:						Tuple 							= 1_5_0
						a_						:						Optional[Any] 							= 1_6
						a_						:						Union[str, Any] 							= (1, 3_8_4, 3_8_4)
						a_						:						Optional[int] 							= False
						a_						:						int 							= """project"""

			if "ade" in checkpoint_url:
						a_						:						Union[str, Any] 							= True
						a_						:						int 							= 7_6_8
						a_						:						Dict 							= [1, 1, 1, 0.5]
						a_						:						Optional[int] 							= 1_5_0
						a_						:						Tuple 							= 1_6
						a_						:						str 							= """huggingface/label-files"""
						a_						:						List[Any] 							= """ade20k-id2label.json"""
						a_						:						Tuple 							= json.load(open(cached_download(hf_hub_url(a__  ,	a__  ,	repo_type="""dataset"""))  ,	"""r"""))
						a_						:						Optional[Any] 							= {int(a__): v for k, v in idalabel.items()}
						a_						:						str 							= idalabel
						a_						:						Optional[Any] 							= {v: k for k, v in idalabel.items()}
						a_						:						Optional[int] 							= [1, 1_5_0, 4_8_0, 4_8_0]

			return config, expected_shape


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Any 							= ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						a_						:						Dict 							= name.replace("""pretrained.model"""  ,	"""dpt.encoder""")
			if "pretrained.model" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.model"""  ,	"""dpt.embeddings""")
			if "patch_embed" in name:
						a_						:						List[str] 							= name.replace("""patch_embed"""  ,	"""""")
			if "pos_embed" in name:
						a_						:						Tuple 							= name.replace("""pos_embed"""  ,	"""position_embeddings""")
			if "attn.proj" in name:
						a_						:						Optional[int] 							= name.replace("""attn.proj"""  ,	"""attention.output.dense""")
			if "proj" in name and "project" not in name:
						a_						:						Tuple 							= name.replace("""proj"""  ,	"""projection""")
			if "blocks" in name:
						a_						:						Any 							= name.replace("""blocks"""  ,	"""layer""")
			if "mlp.fc1" in name:
						a_						:						Tuple 							= name.replace("""mlp.fc1"""  ,	"""intermediate.dense""")
			if "mlp.fc2" in name:
						a_						:						List[str] 							= name.replace("""mlp.fc2"""  ,	"""output.dense""")
			if "norm1" in name and "backbone" not in name:
						a_						:						Dict 							= name.replace("""norm1"""  ,	"""layernorm_before""")
			if "norm2" in name and "backbone" not in name:
						a_						:						str 							= name.replace("""norm2"""  ,	"""layernorm_after""")
			if "scratch.output_conv" in name:
						a_						:						List[Any] 							= name.replace("""scratch.output_conv"""  ,	"""head""")
			if "scratch" in name:
						a_						:						Optional[int] 							= name.replace("""scratch"""  ,	"""neck""")
			if "layer1_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer1_rn"""  ,	"""convs.0""")
			if "layer2_rn" in name:
						a_						:						Any 							= name.replace("""layer2_rn"""  ,	"""convs.1""")
			if "layer3_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer3_rn"""  ,	"""convs.2""")
			if "layer4_rn" in name:
						a_						:						Optional[Any] 							= name.replace("""layer4_rn"""  ,	"""convs.3""")
			if "refinenet" in name:
						a_						:						Tuple 							= int(name[len("""neck.refinenet""") : len("""neck.refinenet""") + 1])
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						a_						:						List[Any] 							= name.replace(f'''refinenet{layer_idx}'''  ,	f'''fusion_stage.layers.{abs(layer_idx-4)}''')
			if "out_conv" in name:
						a_						:						List[Any] 							= name.replace("""out_conv"""  ,	"""projection""")
			if "resConfUnit1" in name:
						a_						:						List[str] 							= name.replace("""resConfUnit1"""  ,	"""residual_layer1""")
			if "resConfUnit2" in name:
						a_						:						Optional[int] 							= name.replace("""resConfUnit2"""  ,	"""residual_layer2""")
			if "conv1" in name:
						a_						:						List[str] 							= name.replace("""conv1"""  ,	"""convolution1""")
			if "conv2" in name:
						a_						:						Optional[Any] 							= name.replace("""conv2"""  ,	"""convolution2""")
			# readout blocks
			if "pretrained.act_postprocess1.0.project.0" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess1.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.0.0""")
			if "pretrained.act_postprocess2.0.project.0" in name:
						a_						:						str 							= name.replace("""pretrained.act_postprocess2.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.1.0""")
			if "pretrained.act_postprocess3.0.project.0" in name:
						a_						:						List[str] 							= name.replace("""pretrained.act_postprocess3.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.2.0""")
			if "pretrained.act_postprocess4.0.project.0" in name:
						a_						:						Any 							= name.replace("""pretrained.act_postprocess4.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.3.0""")

			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.3"""  ,	"""neck.reassemble_stage.layers.0.projection""")
			if "pretrained.act_postprocess1.4" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.4"""  ,	"""neck.reassemble_stage.layers.0.resize""")
			if "pretrained.act_postprocess2.3" in name:
						a_						:						int 							= name.replace("""pretrained.act_postprocess2.3"""  ,	"""neck.reassemble_stage.layers.1.projection""")
			if "pretrained.act_postprocess2.4" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess2.4"""  ,	"""neck.reassemble_stage.layers.1.resize""")
			if "pretrained.act_postprocess3.3" in name:
						a_						:						Union[str, Any] 							= name.replace("""pretrained.act_postprocess3.3"""  ,	"""neck.reassemble_stage.layers.2.projection""")
			if "pretrained.act_postprocess4.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess4.3"""  ,	"""neck.reassemble_stage.layers.3.projection""")
			if "pretrained.act_postprocess4.4" in name:
						a_						:						Tuple 							= name.replace("""pretrained.act_postprocess4.4"""  ,	"""neck.reassemble_stage.layers.3.resize""")
			if "pretrained" in name:
						a_						:						List[Any] 							= name.replace("""pretrained"""  ,	"""dpt""")
			if "bn" in name:
						a_						:						Optional[int] 							= name.replace("""bn"""  ,	"""batch_norm""")
			if "head" in name:
						a_						:						Union[str, Any] 							= name.replace("""head"""  ,	"""head.head""")
			if "encoder.norm" in name:
						a_						:						Any 							= name.replace("""encoder.norm"""  ,	"""layernorm""")
			if "auxlayer" in name:
						a_						:						Any 							= name.replace("""auxlayer"""  ,	"""auxiliary_head.head""")
			if "backbone" in name:
						a_						:						Optional[Any] 							= name.replace("""backbone"""  ,	"""backbone.bit.encoder""")

			if ".." in name:
						a_						:						str 							= name.replace(""".."""  ,	""".""")

			if "stem.conv" in name:
						a_						:						Union[str, Any] 							= name.replace("""stem.conv"""  ,	"""bit.embedder.convolution""")
			if "blocks" in name:
						a_						:						Union[str, Any] 							= name.replace("""blocks"""  ,	"""layers""")
			if "convolution" in name and "backbone" in name:
						a_						:						Dict 							= name.replace("""convolution"""  ,	"""conv""")
			if "layer" in name and "backbone" in name:
						a_						:						Optional[int] 							= name.replace("""layer"""  ,	"""layers""")
			if "backbone.bit.encoder.bit" in name:
						a_						:						Union[str, Any] 							= name.replace("""backbone.bit.encoder.bit"""  ,	"""backbone.bit""")
			if "embedder.conv" in name:
						a_						:						List[str] 							= name.replace("""embedder.conv"""  ,	"""embedder.convolution""")
			if "backbone.bit.encoder.stem.norm" in name:
						a_						:						str 							= name.replace("""backbone.bit.encoder.stem.norm"""  ,	"""backbone.bit.embedder.norm""")
			return name


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for i in range(config.num_hidden_layers):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						a_						:						Optional[int] 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''')
						a_						:						str 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''')
						# next, add query, keys and values (in that order) to the state dict
						a_						:						Any 							= in_proj_weight[: config.hidden_size, :]
						a_						:						List[str] 							= in_proj_bias[: config.hidden_size]
						a_						:						List[str] 							= in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						a_						:						str 							= in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						a_						:						Any 							= in_proj_weight[
						    -config.hidden_size :, :
						]
						a_						:						List[str] 							= in_proj_bias[-config.hidden_size :]


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Dict 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						Union[str, Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_							,					a_						:						str 							= get_dpt_config(a__)
			# load original state_dict from URL
			# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")
			# remove certain keys
			remove_ignore_keys_(a__)
			# rename keys
			for key in state_dict.copy().keys():
						a_						:						List[str] 							= state_dict.pop(a__)
						a_						:						str 							= val
			# read in qkv matrices
			read_in_q_k_v(a__  ,	a__)

			# load HuggingFace model
			a_						:						Any 							= DPTForSemanticSegmentation(a__) if """ade""" in checkpoint_url else DPTForDepthEstimation(a__)
			model.load_state_dict(a__)
			model.eval()

			# Check outputs on an image
			a_						:						Any 							= 4_8_0 if """ade""" in checkpoint_url else 3_8_4
			a_						:						Any 							= DPTImageProcessor(size=a__)

			a_						:						Dict 							= prepare_img()
			a_						:						int 							= image_processor(a__  ,	return_tensors="""pt""")

			# forward pass
			a_						:						Optional[Any] 							= model(**a__).logits if """ade""" in checkpoint_url else model(**a__).predicted_depth

			if show_prediction:
						a_						:						List[str] 							= (
						    torch.nn.functional.interpolate(
						        outputs.unsqueeze(1)  ,	size=(image.size[1], image.size[0])  ,	mode="""bicubic"""  ,	align_corners=a__  ,	)
						    .squeeze()
						    .cpu()
						    .numpy()
						)

						Image.fromarray((prediction / prediction.max()) * 2_5_5).show()

			if pytorch_dump_folder_path is not None:
						Path(a__).mkdir(exist_ok=a__)
						print(f'''Saving model to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)
						print(f'''Saving image processor to {pytorch_dump_folder_path}''')
						image_processor.save_pretrained(a__)

			if push_to_hub:
						model.push_to_hub("""ybelkada/dpt-hybrid-midas""")
						image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""")


if __name__ == "__main__":
	__snake_case				:      str   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
	    type=str,
	    help="""URL of the original DPT checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=str,
	    required=False,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	parser.add_argument(
	    """--model_name""",
	    default="""dpt-large""",
	    type=str,
	    help="""Name of the model, in case you're pushing to the hub.""",
	)
	parser.add_argument(
	    """--show_prediction""",
	    action="""store_true""",
	)

	__snake_case				:      str   		=			parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)


